FaultCDR: A Cross-Disentangled Representation Learning Method for 3-D Fault Detection
Ruonan Yin, Kewen Li, Zhifeng Xu, Zongchao Huang, Xinyuan Zhu · IEEE Transactions on Geoscience and Remote Sensing · 2024
Fault detection is a crucial step in seismic interpretation, which can be regarded as a segmentation task in computer vision. Existing deep learning methods train models using synthetic data. However, due to differences between synthetic and field data in signal-to-noise ratio (SNR), seismic resolution, and fault orientation, models trained on synthetic data may yield unreliable results when applied to field data. In this article, we assume that the features required for fault detection are irrelevant to nonfault features such as SNR and propose a cross-disentangled representation learning method for 3-D fault detection, called FaultCDR. FaultCDR comprises a fault encoder, a nonfault encoder, a seismic reconstructor, and a segmenter. It employs a cross-disentangled representation mechanism to decouple fault features and nonfault features. The cross-disentangled representation mechanism is achieved through the seismic reconstruction task of remixed fault/nonfault features and a self-supervised feature consistency task. The proposed orthogonal loss is used to ensure that fault features and nonfault features are irrelated. The decoupled pure fault features are finally fed into the segmenter for fault detection. Through intro-database and cross-database testing, we demonstrated the stability and generalization of FaultCDR in fault detection across different datasets. Comparative experiments with existing state-of-the-art (SOTA) fault detection methods reveal that FaultCDR achieves superior performance in both detection accuracy and visual quality.